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---
library_name: transformers
license: mit
datasets:
- CodeGoat24/UniGenBench-Eval-Images
base_model:
- CodeGoat24/UnifiedReward-2.0-qwen-72b
---
# UniGenBench-EvalModel-qwen-72b-v1
This model is tailored for offline T2I model evaluation on [UniGenBench](https://github.com/CodeGoat24/UniGenBench), which achieves an average accuracy of 94% compared to evaluations by Gemini 2.5 Pro.
Feel free to use this model to assess and compare the performance of your models.
For further details, please refer to the following resources:
- 📰 Paper: https://arxiv.org/pdf/2508.20751
- 🪐 Project Page: https://codegoat24.github.io/UnifiedReward/Pref-GRPO
- 🤗 UniGenBench: https://github.com/CodeGoat24/UniGenBench
- 🤗 Leaderboard: https://huggingface.co/spaces/CodeGoat24/UniGenBench_Leaderboard
- 👋 Point of Contact: [Yibin Wang](https://codegoat24.github.io)
## Citation
```bibtex
@article{UniGenBench++,
title={UniGenBench++: A Unified Semantic Evaluation Benchmark for Text-to-Image Generation},
author={Wang, Yibin and Li, Zhimin and Zang, Yuhang and Bu, Jiazi and Zhou, Yujie and Xin, Yi and He, Junjun and Wang, Chunyu and Lu, Qinglin and Jin, Cheng and others},
journal={arXiv preprint arXiv:2510.18701},
year={2025}
}
@article{UniGenBench,
title={Pref-GRPO: Pairwise Preference Reward-based GRPO for Stable Text-to-Image Reinforcement Learning},
author={Wang, Yibin and Li, Zhimin and Zang, Yuhang and Zhou, Yujie and Bu, Jiazi and Wang, Chunyu and Lu, Qinglin, and Jin, Cheng and Wang, Jiaqi},
journal={arXiv preprint arXiv:2508.20751},
year={2025}
}
``` |